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  <div class="section" id="numpy-in1d">
<h1>numpy.in1d<a class="headerlink" href="#numpy-in1d" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="numpy.in1d">
<code class="sig-prename descclassname">numpy.</code><code class="sig-name descname">in1d</code><span class="sig-paren">(</span><em class="sig-param">ar1</em>, <em class="sig-param">ar2</em>, <em class="sig-param">assume_unique=False</em>, <em class="sig-param">invert=False</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/numpy/numpy/blob/v1.18.1/numpy/lib/arraysetops.py#L484-L594"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#numpy.in1d" title="Permalink to this definition">¶</a></dt>
<dd><p>Test whether each element of a 1-D array is also present in a second array.</p>
<p>Returns a boolean array the same length as <em class="xref py py-obj">ar1</em> that is True
where an element of <em class="xref py py-obj">ar1</em> is in <em class="xref py py-obj">ar2</em> and False otherwise.</p>
<p>We recommend using <a class="reference internal" href="numpy.isin.html#numpy.isin" title="numpy.isin"><code class="xref py py-func docutils literal notranslate"><span class="pre">isin</span></code></a> instead of <a class="reference internal" href="#numpy.in1d" title="numpy.in1d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">in1d</span></code></a> for new code.</p>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<dt><strong>ar1</strong><span class="classifier">(M,) array_like</span></dt><dd><p>Input array.</p>
</dd>
<dt><strong>ar2</strong><span class="classifier">array_like</span></dt><dd><p>The values against which to test each value of <em class="xref py py-obj">ar1</em>.</p>
</dd>
<dt><strong>assume_unique</strong><span class="classifier">bool, optional</span></dt><dd><p>If True, the input arrays are both assumed to be unique, which
can speed up the calculation.  Default is False.</p>
</dd>
<dt><strong>invert</strong><span class="classifier">bool, optional</span></dt><dd><p>If True, the values in the returned array are inverted (that is,
False where an element of <em class="xref py py-obj">ar1</em> is in <em class="xref py py-obj">ar2</em> and True otherwise).
Default is False. <code class="docutils literal notranslate"><span class="pre">np.in1d(a,</span> <span class="pre">b,</span> <span class="pre">invert=True)</span></code> is equivalent
to (but is faster than) <code class="docutils literal notranslate"><span class="pre">np.invert(in1d(a,</span> <span class="pre">b))</span></code>.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 1.8.0.</span></p>
</div>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>in1d</strong><span class="classifier">(M,) ndarray, bool</span></dt><dd><p>The values <em class="xref py py-obj">ar1[in1d]</em> are in <em class="xref py py-obj">ar2</em>.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="numpy.isin.html#numpy.isin" title="numpy.isin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">isin</span></code></a></dt><dd><p>Version of this function that preserves the shape of ar1.</p>
</dd>
<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.lib.arraysetops</span></code></dt><dd><p>Module with a number of other functions for performing set operations on arrays.</p>
</dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p><a class="reference internal" href="#numpy.in1d" title="numpy.in1d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">in1d</span></code></a> can be considered as an element-wise function version of the
python keyword <em class="xref py py-obj">in</em>, for 1-D sequences. <code class="docutils literal notranslate"><span class="pre">in1d(a,</span> <span class="pre">b)</span></code> is roughly
equivalent to <code class="docutils literal notranslate"><span class="pre">np.array([item</span> <span class="pre">in</span> <span class="pre">b</span> <span class="pre">for</span> <span class="pre">item</span> <span class="pre">in</span> <span class="pre">a])</span></code>.
However, this idea fails if <em class="xref py py-obj">ar2</em> is a set, or similar (non-sequence)
container:  As <code class="docutils literal notranslate"><span class="pre">ar2</span></code> is converted to an array, in those cases
<code class="docutils literal notranslate"><span class="pre">asarray(ar2)</span></code> is an object array rather than the expected array of
contained values.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 1.4.0.</span></p>
</div>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">test</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">states</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">mask</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">in1d</span><span class="p">(</span><span class="n">test</span><span class="p">,</span> <span class="n">states</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">mask</span>
<span class="go">array([ True, False,  True, False,  True])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">test</span><span class="p">[</span><span class="n">mask</span><span class="p">]</span>
<span class="go">array([0, 2, 0])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">mask</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">in1d</span><span class="p">(</span><span class="n">test</span><span class="p">,</span> <span class="n">states</span><span class="p">,</span> <span class="n">invert</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">mask</span>
<span class="go">array([False,  True, False,  True, False])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">test</span><span class="p">[</span><span class="n">mask</span><span class="p">]</span>
<span class="go">array([1, 5])</span>
</pre></div>
</div>
</dd></dl>

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